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Archive of posts filed under the Viz category.

AGU Goes to NOL

best to all in the next week e.g. Oak Ridge National Labs research group LINK

NAIP CA 2016 Processing

Running 11,000 DOQQs through a processing pipeline – so far, so good !       Details at 0.6 meters per pixel:     Prioritize LA, for Today auth_buildings=# update doqq_processing as a set priority=6 from tl_2016_us_county b, naip_3_16_1_1_ca c where b.statefp=’06’ and b.countyfp=’037′ and st_intersects(b.geom, c.geom) and a.doqqid=c.gid;

OSM Fresno

In Openstreetmap US, California Fresno area, a controversial [0] series of imports of legal property records (aka PARCEL) are mixed in with other POLYGONS. Many various POLYGON in Fresno now share the tag landuse=residential, both the PARCEL legal records and real building footprint POLYGON, as well as various others. After reviewing the wiki talk page, […]

Winter California 2015

For those that have been following the Climate Change story over the years, this satellite imagery tells a story quite vividly.. no modelling uncertainty involved.

Census Tract and 150 Meter Grids Compare

In this screenshot of Central Silicon Valley, Census tracts have been combined with a constraints layer, and then cut with a 150 meter grid in the EPSG:3310 projection. Using imputation tables and external sources, each grid cell is then computed. The result is a statistically defensible, higher-resolution and handily applicable set of grid cells.

ACS 5yr Viz Processing

A systematic way to choose, extract and visualize data from the massive American Community Survey 5 Year census product is a challenge. I have written python code to ingest raw inputs into tables, and a small relational engine to handle the verbose naming. An extraction and visualization process is underway… something like the following: 0) […]

Worldwide Forestry Inventory Published, Nov13

Dozens of major news outlets posted articles yesterday profiling a paper published in the journal ‘Science’ by a team led by Matthew Hansen, a remote sensing scientist at the University of Maryland, along with extensive data. ‘Published by Hansen, Potapov, Moore, Hancher et al. * Powered by Google Earth Engine‘

NLCD 06 Landcover, San Francisco Bay Area

A colleague pointed out the National Land Cover Database (NLCD) imagery today, which is not new, but it is useful. Here is a simple treatment of the San Francisco Bay Area, with city center markers matching the red urban coloring used in the base map. Click for the larger image, and you can see Lake […]

California – A Regional Approach

A simple spatial classification of geo-data, by federated county; based on the regional planning infrastructure in California.   The hilited areas include:   Sacramento Area Council of Governments AB32 Scoping Plan Update Regional Profiles – All   San Diego Association of Governments  last but not least, at the State level     a simple numbering system via […]

Distance to Nearest OSM Road

osm_ca_20=# SELECT   osm_id,   geometry < -> st_geomfromEWKT( ‘SRID=900913;POINT(-13145550 4045137)’ ) as the_distance FROM   osm_new_mainroads ORDER BY   geometry < -> st_geomfromEWKT( ‘SRID=900913;POINT(-13145550 4045137)’ ) limit 1; osm_id | the_distance ———-+—————— 59339590 | 268.205611425265 (1 row) UPDATE distance of all museums less than 30km away from a school, by school. Thanks to the geonames project for the […]